Edge Computing and Serverless are set to redefine the DevOps processes to deal with Machine Learning models. While the heavy lifting for ML will be done in the cloud, the edge layer will simplify the deployment experience and Serverless will streamline the developer experience.

Machine Learning is slowly but steadily taking over the cloud. The adoption of this brand-new cloud will not only result in better revenues for the providers but also helps them deliver better capabilities that are driven by data.

In today’s fast-moving cybersecurity world, by the time you wipe one virus from the company laptops, hackers may have created a more lethal one. To deal with this increasingly dangerous environment — and to save money — many companies outsource their cybersecurity.

Fixing the need for an analytics and business intelligence dashboarding tool that was both affordable and easy to use. Cyfe, founded in 2012, pioneered the freemium dashboard platform, expanding a year later to include further support for analytic and marketing tools like Eventbrite, Stripe, and many others. Now, the company is helping brands like WholeFoods, Marriott, ABC, and Groupon save tons of time and effort in their marketing and IT departments.

Entrepreneurs in financial services companies say businesses like theirs are doing a lot more than making established Western banks worried about their market share. During a panel discussion at SxSW here this weekend, they argued that they’re helping create a new middle class in the developing world, with tremendous consequences for the global economy.

Leading elevator and escalator engineering and maintenance company Kone is using machine learning algorithms on their Internet of Things (IoT) and machine data to perform real-time reporting and predictive maintenance. It even allows people to listen in on machine-to-machine conversations.

Consumer’s preferences are changing, along with the capabilities of connected cars. That means automakers will have to continually improve how they utilize predictive analytics, IoT and artificial intelligence technology, in order to expand and re-invigorate their offerings.

These insights are from an excellent research study by Dresner Advisory Services titled 2017 Location Intelligence Market Study Report. Dresner Advisory Services defines location intelligence as a form of business intelligence where the dominant dimension used for analysis is location or geography.

Many traditonal performance management processes in companies are broken, leading to biased results and wrong performance assessments. Here we look at how big data and analytics can be used by HR teams to more accurately measure and monitor performance.

Artificial intelligence and machine learning algorithms are increasingly used by companies to make decisions about us, but often without revealing the secret code that helps them to make the decisions. However, two researchers from Oxford argue that a new law could challenge all this.

The big news at HPE Discover London 2016 was that HPE turned its architectural prototype of “The Machine” just two weeks prior. The Machine is now an operational R&D testbed for investigating next generation data center architectures.

Deep Learning is not only a massive buzzword spanning business and technology but also a concept that will transform most industries and jobs, as well as the way we live our lives. However, there is confusion about what it is and how it differs from Machine Learning and Artificial Intelligence (AI). Over the past few years, the term “deep learning” has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics.

As cyberattacks and data breaches become an ever-more common side effect of life in the digital era, the role of social engineering and the historical prioritization of user experience over security in facilitating those attacks has become a huge focal area of the cybersecurity industry. Yet, it is not just the digital world that is vulnerable to social engineering – almost every process we undertake in our daily lives is being impacted by technological evolution that far too often places ease-of-use over security.

Real-time marketing techniques, personalization, the use of contextual clues, and the rapid convergence of marketing technology (Martech) and advertising technology (Adtech) are four key forces driving the future of data-centric marketing. The 2016 Hype Cycle for Digital Marketing and Advertising reflects how the combined effect of these four forces are fueling innovative new uses of analytics, contextual clues enabled by Internet of Things (IoT) devices, machine learning, personalization and a more data-centric approach to driving marketing strategies.

Machine learning is a buzzword in the technology world right now, and for good reason: It represents a major step forward in how computers can learn. Very basically, a machine learning algorithm is given a “teaching set” of data, then asked to use that data to answer a question.

68% of manufacturers are currently investing in data analytics. 46% of manufacturers agree that implementing and using data analytics is no longer optional. 32% see the potential for big data analytics and Industrial Internet of Things (IIoT) to improve supply chain performance and increase revenue.

There's a fundamental difference between companies that apply digital technology as a bolt-on (frequently adding an eCommerce site, social media, or customer mobile apps) and those that take a more holistic approach to transforming the way the company uses technology to deliver better customer outcomes and drive revenue. Transformers are more likely to succeed because they recognize their customers’ expectations are evolving.

2015 worldwide spending on digital commerce platform software reached approximately $4.7B, attaining 15% year-over-year increase according to Gartner. Gartner estimates that the digital commerce platform market will grow at a Compound Annual Growth Rate (CAGR) of over 15% from 2015 through 2020, including revenue from SaaS, licenses, and maintenance.

With the advent of natural language processing (NLP) technologies and the ability to understand more unstructured data, like phone call recordings, companies are sitting on a wealth of information every time they record a call. And because the field is expanding so rapidly, there are many different ways companies can put this data to use.

As businesses continue their flight to the cloud, their concerns about security are changing. The cloud can offer companies better security than their own data centers — but only if they understand how to manage the responsibilities that come with it. When it comes to the physical environment, for small and mid-sized businesses, the cloud often provides better security than an on-premise data center, said Paul Hill, a senior security consultant with SystemExperts, an IT compliance and security firm.